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随机过程与确定性过程共同驱动颗粒浮选过程中厌氧微生物群落的组装。

Combined Stochastic and Deterministic Processes Drive Community Assembly of Anaerobic Microbiomes During Granule Flotation.

作者信息

Trego Anna Christine, McAteer Paul G, Nzeteu Corine, Mahony Therese, Abram Florence, Ijaz Umer Zeeshan, O'Flaherty Vincent

机构信息

Microbial Ecology Laboratory, Microbiology, School of Natural Sciences and Ryan Institute, National University of Ireland, Galway, Ireland.

Functional Environmental Microbiology, Microbiology, School of Natural Sciences and Ryan Institute, National University of Ireland Galway, Galway, Ireland.

出版信息

Front Microbiol. 2021 May 14;12:666584. doi: 10.3389/fmicb.2021.666584. eCollection 2021.

Abstract

Advances in null-model approaches have resulted in a deeper understanding of community assembly mechanisms for a variety of complex microbiomes. One under-explored application is assembly of communities from the built-environment, especially during process disturbances. Anaerobic digestion for biological wastewater treatment is often underpinned by retaining millions of active granular biofilm aggregates. Flotation of granules is a major problem, resulting in process failure. Anaerobic aggregates were sampled from three identical bioreactors treating dairy wastewater. Microbiome structure was analysed using qPCR and 16S rRNA gene amplicon sequencing from DNA and cDNA. A comprehensive null-model approach quantified assembly mechanisms of floating and settled communities. Significant differences in diversity were observed between floating and settled granules, in particular, we highlight the changing abundances of and . Both stochastic and deterministic processes were important for community assembly. Homogeneous selection was the primary mechanism for all categories, but dispersal processes also contributed. The lottery model was used to identify clade-level competition driving community assembly. Lottery "winners" were identified with different winners between floating and settled groups. Some groups changed their winner status when flotation occurred. , for example, was only a winner in settled biomass (cDNA-level) and lost its winner status during flotation. Alternatively, gained winner status during flotation. This analysis provides a deeper understanding of changes that occur during process instabilities and identified groups which may be washed out-an important consideration for process control.

摘要

零模型方法的进展使人们对各种复杂微生物群落的组装机制有了更深入的理解。一个尚未充分探索的应用是建筑环境中群落的组装,尤其是在过程干扰期间。生物废水处理的厌氧消化通常依赖于保留数百万个活性颗粒生物膜聚集体。颗粒的浮选是一个主要问题,会导致过程失败。从处理乳制品废水的三个相同生物反应器中采集厌氧聚集体。使用qPCR和来自DNA和cDNA的16S rRNA基因扩增子测序分析微生物群落结构。一种全面的零模型方法量化了漂浮和沉降群落的组装机制。在漂浮颗粒和沉降颗粒之间观察到多样性的显著差异,特别是,我们突出了[具体物种1]和[具体物种2]丰度的变化。随机过程和确定性过程对群落组装都很重要。均匀选择是所有类别中的主要机制,但扩散过程也有贡献。彩票模型用于识别驱动群落组装的进化枝水平竞争。彩票“赢家”在漂浮组和沉降组之间有所不同。一些群体在浮选发生时改变了其赢家地位。例如,[具体物种1]仅在沉降生物量(cDNA水平)中是赢家,在浮选期间失去了赢家地位。或者,[具体物种2]在浮选期间获得了赢家地位。该分析提供了对过程不稳定期间发生的变化的更深入理解,并识别了可能被冲走的群体——这是过程控制的一个重要考虑因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6463/8160314/60e252b56578/fmicb-12-666584-g001.jpg

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